可比性
显微镜
显微镜
管道(软件)
超分辨率
标杆管理
人工智能
计算机科学
分辨率(逻辑)
生物
计算机视觉
光学
图像(数学)
数学
物理
业务
营销
组合数学
程序设计语言
作者
Markus Mund,Jonas Ries
标识
DOI:10.1091/mbc.e19-04-0189
摘要
Superresolution microscopy is becoming increasingly widespread in biological labs. While it holds enormous potential for biological discovery, it is a complex imaging technique that requires thorough optimization of various experimental parameters to yield data of the highest quality. Unfortunately, it remains challenging even for seasoned users to judge from the acquired images alone whether their superresolution microscopy pipeline is performing at its optimum, or if the image quality could be improved. Here, we describe how superresolution microscopists can objectively characterize their imaging pipeline using suitable reference standards, which are stereotypic so that the same structure can be imaged everywhere, every time, on every microscope. Quantitative analysis of reference standard images helps characterizing the performance of one’s own microscopes over time, allows objective benchmarking of newly developed microscopy and labeling techniques, and finally increases comparability of superresolution microscopy data between labs.
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